ChatVector-AI

ChatVector-AI is an open-source Retrieval-Augmented Generation (RAG) engine for ingesting, indexing, and querying unstructured documents such as PDFs and text files. Think of it as an engine developers can use to build document-aware applications — such as research assistants, contract analysis tools, or internal knowledge systems — without having to reinvent the RAG pipeline.

ChatVector-AI provides a clean, extensible backend foundation for RAG-based document intelligence. It handles the full lifecycle of document Q&A: document ingestion (PDF, text), text extraction and chunking, vector embedding and storage, semantic retrieval, and LLM-powered answer generation.

The goal is to offer a developer-focused RAG engine that can be embedded into other applications, tools, or products — not a polished end-user SaaS. It's designed as a production-ready backend engine with batteries-included architecture, providing a fully functional FastAPI service with logging, testing, and a clean API.

• Built production-ready FastAPI backend with Uvicorn ASGI server for high performance
• Implemented full document lifecycle: PDF extraction, chunking, vector embeddings, and semantic search
• Integrated Supabase PostgreSQL with pgvector for native vector similarity search
• Leveraged Google AI Studio (Gemini) for LLM-powered answer generation and embeddings
• Designed clean, extensible architecture focused on clarity, debuggability, and production deployment
• Provided complete RAG pipeline as an embeddable engine for document intelligence applications
• Created developer-focused solution with automatic OpenAPI docs and observability patterns
• Open-sourced for developers building research assistants, contract analysis tools, and knowledge systems